AbstractThe focus of this thesis is to determine if a novel multi-disciplinary fuel cell electric vehicle model can accurately predict energy consumption for electric vehicles and hydrogen fuel cell electric vehicles. This is important in order for fuel cell electric vehicle development to take place within the virtual environment and to speed up the route to market for new vehicle technologies.
The thesis has met this research aim though an extensive study of relevant literature, the implementation of state-of-the-art modelling methodologies using the software tool Dymola, the validation of each model using test data, by comparing test results for type approval methods against real-world test data, and by showing the capabilities of the developed model.
The research produced several key findings: There are no multi-disciplinary fuel cell electric vehicle models within the public domain; The electric vehicle version of the developed model can represent vehicle energy consumption on both the New European Drive Cycle and the Coventry University Drive Cycle to 1.3%; The fuel cell electric vehicle model can represent the vehicle energy consumption on the Coventry University Drive Cycle to 8%; The difference in energy consumption between a real-world practical drive cycle and the typical type approval practical test is 36%, where the real-world drive cycle requires 36% more energy per kilometre; The difference in energy consumption estimation between conventional type approval methods and the proposed method is 40%, where the conventional method under predicts energy consumption for real-world driving conditions.
The main outcome from this research is that the current approaches to estimate energy consumption in the simulation environment fails to provide accurate results against real world data, because they do not embrace the varying loads associated with driving on a real road. This work has resulted in a novel multi-disciplinary model that can represent electric vehicles and fuel cell electric vehicles to accurately predict energy consumption for real-world driving conditions.
|Date of Award||Feb 2019|
|Supervisor||Mike Blundell (Supervisor), Bernard Porter (Supervisor), Jinlei Shang (Supervisor) & John Jostins (Supervisor)|